Hi Paul, Currently it does not provide prediction intervals, as it is not assuming a generative model or a particular error distribution. I think the best way forward, with nnfor, is to construct empirical ones. Have a look at this paper for some relatively straightforward approaches that work quite well under a variety of conditions. The paper looks at safety stocks, but the same approach can be used for generating prediction intervals.https://kourentzes.com/forecasting/2018/06/20/empirical-safety-stock-estimation-based-on-kernel-and-garch-models/ Best,Nikos
Professor, Dept. Management Science Lancaster University Management School, UK Centre for Marketing Analytics & Forecasting blog: http://nikolaos.kourentzes.com twitter: @nkourentz Book: Ord, Fildes & Kourentzes (2017) Principles of Business Forecasting (2nd ed.), Wessex. On Tuesday, August 27, 2019, 3:44:45 PM GMT+3, Paul Bernal <paulberna...@gmail.com> wrote: Dear friends, Hope you are all doing well. I am currently using function mlp (to fit multiple layer percentron model) to generate forecasts using package nnfor. I would like to know if the mlp function provides, or is there a way to construct confidence intervals for the forecasts generated by this mlp function. Any help and/or guidance would be much appreciated, Best regards, Paul [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
